With the advent of uavs, new potential applications emerge for unconstrained images and videos analysis for aerial view cameras, ranging from agriculture to security. Visual tasks like object detection, classification, or segmentation are the essential building blocks for many of them.
In this section, we address the counting problem for evaluating the number of vehicles present in parking lots from uav imagery, i.e., images of parking areas taken from a drone view. In this scenario, the cars appear in various orientations, often within the same scene, and can be partially occluded by trees or bridges. Other challenges are represented by clutters caused by motorbikes, buildings, or different light conditions. To this end, we propose a real-time counting solution to be used directly onboard drones equipped with low-power vision embedded systems. Specifically, we relied on an existing CNN-based detection framework that we re-trained, specializing it for this specific task and making it more suitable for working in scenarios constrained by limited computational capabilities. We evaluated the effectiveness and the reliability of our solution, testing it on two publicly available car counting datasets: the CARPK dataset and the PUCPR+ dataset [1]}. In both cases, we achieved state-of-the-art counting errors. For our tests, we used an NVIDIA Jetson TX2 Developer Kit, a power-efficient embedded ai computing device running at 4 Frames Per Second (FPS). An overview of the system architecture is shown in fig:dronearchitecture
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